Khadija Abdulhadi

Hey everyone,

I've been building a labelling tool, a desktop app (PyQt6) for capturing and annotating gesture datasets think of it as a companion tool for gesture recognition pipelines like Gesto, making it easier to collect, label, and export hand landmark data for training models.

I'm at a fork in the road on direction, and I'd love your input:

1. Open source it release it freely (currently GPL-3.0), let the community use it, contribute, and shape where it goes.

We launched Mira on Product Hunt today, and the conversations in the comments have been the most interesting part of the whole day.

Researchers asked about probe neutrality whether the follow-up questions an AI asks mid-interview can lead a participant, rather than uncover them. They asked about cultural calibration, whether emotion models trained largely on Western data can accurately read a participant in Jakarta or Nairobi. They asked whether the say/feel mismatch gets surfaced as raw evidence or quietly resolved into a single confidence score.

These are serious questions. And they made me realize something: the bar for trust in AI-moderated research is fundamentally different from other AI tools.

If a writing assistant gets something wrong, you catch it before you publish. If an AI coding tool hallucinates, your tests fail. But if a research tool misreads how participants felt during a concept test, and that feeds into a product decision, the error is invisible. By the time the product ships and the market responds, the research moment is long gone.

Building an API that pulls data across 13 social platforms, and the thing that's eaten the most engineering time isn't the scraping itself, it's how differently every platform breaks. Rate limits that change without notice, auth flows that expire silently, structured data one week and a wall of JS the next.

Curious what's bitten other builders here. Which platform has been the worst to depend on, and what did you end up doing about it?